fastforks 0.1.0

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README.md

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+---
+library_name: "fastforks"
+pipeline_tag: "depth-estimation"
+license: "other"
+license_name: "noncommercial"
+license_link: "https://github.com/Junda24/MonSter-plusplus/blob/9167101af8fbe93c706707daa7a8181089c8b1b4/LICENSE#L34"
+tags:
+- "stereo"
+- "stereo-matching"
+- "disparity"
+- "cuda"
+- "pytorch"
+- "fastforks"
+---
+# RT-MonSter++ (fastforks)
+
+RT-MonSter++ (Cheng et al. 2025), the real-time MonSter++: Depth Anything V2 (ViT-S) monocular branch + coarse-to-fine (1/16, 1/8, 1/4) local geometry volumes, stereo and mono ConvGRUs, 4 updates.
+
+This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: <https://github.com/kleinicke/fastforks>. Kernels for the `kernels` backend: <https://huggingface.co/fastforks/kernels>. Original code: <https://github.com/Junda24/MonSter-plusplus> at `9167101af8fb`.
+
+Author weight repos: <https://huggingface.co/cjd24/MonSter-plusplus>.
+
+Task: stereo. Tier: experimental. Outputs: `disparity`. Backends: `portable`, `kernels`.
+
+Input: a rectified left / right image pair. Output: a disparity map (pixels, left view).
+
+## Variants
+
+Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in [`card.toml`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/stereo/models/rt_monster_plusplus/card.toml).
+
+| variant | weights (author link) | SHA-256 | weights licence | commercial use |
+|---|---|---|---|---|
+| `rt-monster-plusplus-mix-all@1` | [Zero_shot.pth](https://huggingface.co/cjd24/MonSter-plusplus/resolve/0c140353fa4b0485f08a9d18c2a5c0735188a84b/Zero_shot.pth?download=true) | `0741dd6f622b` | LicenseRef-noncommercial | **no** |
+
+## Licence
+
+Original-code strings, recorded as they are (research-use package; check them before any other use). Code: LicenseRef-noncommercial. Files: `LICENSE`.
+
+Training data: mix_all (Zero_shot.pth): 'all the datasets we collect over 2M image pairs' (README), not listed.
+
+Licence evidence: <https://github.com/Junda24/MonSter-plusplus/blob/9167101af8fbe93c706707daa7a8181089c8b1b4/LICENSE#L34>
+
+## Input size
+
+any H x W >= 32, padded to a multiple of 32
+Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `rt-monster-plusplus-mix-all` 1988x2880 (9.3 GiB peak allocated, 11.1 reserved)
+
+## Speed and memory
+
+### Stereo, Middlebury 2014 half, 994x1482
+
+| model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `rt-monster-plusplus-mix-all` | 252 | 251 | 113 | 94.1 | 2.7x | 1.2x | 2922 / 3044 | 2592 / 2646 | 2704 / 3976 |
+
+### Startup and first call
+
+| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
+| --- | --- | --- | --- | --- | --- | --- |
+| `rt-monster-plusplus-mix-all` | 0.99 (375x1242) | 0.26 | 0.23† | 0.66 (375x1242) | 0.13† | 0.10† |
+
+### torch.compile and TensorRT
+
+| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `rt-monster-plusplus-mix-all` | 994x1482 | 113 | 77.0 | 69.4 | 44.1 | 150 | 94.1 | 2592 / 2646 | 2750 / 2942 | 952 |
+
+### Fixed-size speed and peak memory, all models
+
+| model | mode | 480x640 | 720x1280 | 1080x1920 | 12 MP 3000x4000 |
+| --- | --- | --- | --- | --- | --- |
+| `rt-monster-plusplus-mix-all` | portable | 31.2 ms / 609 / 720 | 67.1 ms / 1625 / 1968 | 168.0 ms / 3478 / 4148 | OOM |
+| `rt-monster-plusplus-mix-all` | kernels | 18.3 ms / 630 / 1934 | 57.5 ms / 1660 / 1998 | 140.1 ms / 3545 / 4188 | OOM |
+
+Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
+
+Hardware, protocol and caveats: [benchmarks](https://github.com/kleinicke/fastforks/blob/main/docs/benchmarks.md) (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or `(fp32)` where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's.
+
+## Accuracy
+
+### On public datasets
+
+Original = the authors' code, same checkpoint and protocol.
+
+| variant | dataset | original fp32 | original fp16 | portable | kernels | note |
+|---|---|---|---|---|---|---|
+| `rt-monster-plusplus-mix-all` | ETH3D two-view train, bad > 1 px % / EPE | 2.05 / 0.226 | 2.05 / 0.226 | 2.05 / 0.226 | 2.05 / 0.226 | possibly in the training data |
+| `rt-monster-plusplus-mix-all` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 11.01 / 1.988 | 11.01 / 1.988 | 11.00 / 1.989 | 11.02 / 1.998 | possibly in the training data |
+
+Measured on portable: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-05; kernels: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-06.
+
+### Parity with the original code
+
+Every variant is checked against reference outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.02 px and p99 <= 0.25 px.
+
+## Usage
+
+```python
+from fastforks import stereo
+
+# non-commercial weights: run with FASTFORKS_NONCOMMERCIAL=1 in the environment
+model = stereo.load("rt-monster-plusplus-mix-all")  # mode="auto": the preferred backend from recorded measurements
+result = model("left.png", "right.png")
+result.disparity  # [H, W] float32 tensor, input resolution
+result.backend  # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
+```
+
+Install with `pip install fastforks` (see <https://github.com/kleinicke/fastforks>). Other variants: `rt-monster-plusplus-mix-all`.
+
+## Kernels backend
+
+`stereo.load("rt-monster-plusplus-mix-all", mode="kernels")` runs our CUDA kernels (`igev`, `cutlass_conv`, `cudnn_hacc`, `fastbb`). It needs a prebuilt kernel for your GPU architecture (downloaded from <https://huggingface.co/fastforks/kernels> on first use) and a recent NVIDIA driver; if there is none it raises `BackendUnavailableError` with the reason and never falls back silently. `mode="auto"` picks `kernels` when it can run and logs the choice.
+
+<!-- staged from fastforks feccac41 (2026-11-06) -->